Tablets, laptops, phones (e.g., cellular or satellite), mobile (vehicular) or portable (personal) two-way radios, and other communication devices are now in common use by users, such as first responders (including firemen, police officers, and paramedics, among others), and provide such users and others with instant access to increasingly valuable information and resources such as vehicle histories, arrest records, outstanding warrants, health information, real-time traffic or other situational status information, and any other information that may aid the user in making a more informed determination of an action to take or how to resolve a situation, among other possibilities.
In addition, video coverage of many major metropolitan areas is reaching a point of saturation such that nearly every square foot of some cities is under surveillance by at least one static or moving camera. Currently, some governmental public safety and enterprise security agencies are deploying government-owned and/or privately-owned cameras or are obtaining legal access to government-owned and/or privately-owned cameras, or some combination thereof, and are deploying command centers to monitor these cameras. Additionally, such command centers may implement machine learning models to automatically detect certain events or situations in real-time video and/or audio streams and/or in previously captured video and/or audio streams generated from the monitored cameras.
However, as the number of audio and/or video streams increases, and the number of events to be detected and number of corresponding machine learning models involved correspondingly increases, it becomes difficult and time-consuming to train, update, and verify correct output of such models with respect to new situations, new actions, new types of cameras, new lighting situations, and other parameters, such that the increased value of such audio and/or video monitoring and the ability to identify situations of concern via machine learning models decreases substantially.
Skilled artisans will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of embodiments of the present invention.
The apparatus and method components have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein.